Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4974023 | Journal of the Franklin Institute | 2017 | 19 Pages |
Abstract
In this article, the non-fragile finite-time Hâ state estimation problem of neural networks is discussed with distributed time-delays. Based on a modified Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique, a novel delay-dependent criterion is presented such that the error system is finite-time boundedness with guaranteed Hâ performance. In order to obtain less conservative results, Wirtingers integral inequality and reciprocally convex approach are employed. The estimator gain matrix can be achieved by solving the LMIs. Finally, Numerical examples are given to demonstrate the effectiveness of the proposed approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
M. Syed Ali, S. Saravanan, Quanxin Zhu,